Closed DebapriyaHazra closed 1 year ago
Problem 1: The default yaml configuration is an absolute path, which can be changed to the directory where you store the weights, such as ./hugging_cache/llama-2-7b
, or directly use the online download method of meta-llama/ LLAMA-2-7B-HF
.
Problem 2: Since the original dataset is about 1K pieces, the above script takes about 1-2h to run. If you only want to verify the validity of the method, you can set ds_size
to 10 and edit only 10 samples.
See https://github.com/zjunlp/EasyEdit/blob/main/examples/run_zsre_llama2.py#L17
Thank you so much for your response.
@pengzju 请问假如我需要编辑llama2-7b模型,我需要从huggingface的llama2-7b仓库中下载哪些文件到./hugging_cache/llama-2-7b中呢?需要下载所有文件吗?谢谢!
正常来讲,您只需要下载蓝框内的文件。包括模型config,tokenizer config,模型权重等
感谢您的答复,谢谢!
Hello,
While trying to run python run_zsre_llama2.py \ --editing_method=MEMIT \ --hparams_dir=../hparams/MEMIT/llama-7b \ --data_dir=./data
I am getting the error:
python3.9/site-packages/datasets/builder.py", line 539, in _create_builder_config raise ValueError( ValueError: BuilderConfig '20200501.en' not found. Available: ['20220301.aa', '20220301.ab', '20220301.ace', '20220301.ady', '20220301.af', '20220301.ak', '20220301.als', '20220301.am', '20220301.an', '20220301.ang', '20220301.ar', '20220301.arc', '20220301.arz', '20220301.as', '20220301.ast', '20220301.atj', '20220301.av', '20220301.ay', '20220301.az', '20220301.azb', '20220301.ba', '20220301.bar', '20220301.bat-smg', '20220301.bcl', '20220301.be', '20220301.be-x-old', '20220301.bg', '20220301.bh', '20220301.bi', '20220301.bjn', '20220301.bm', '20220301.bn', '20220301.bo', '20220301.bpy', '20220301.br', '20220301.bs', '20220301.bug', '20220301.bxr', '20220301.ca', '20220301.cbk-zam', '20220301.cdo', '20220301.ce', '20220301.ceb', '20220301.ch', '20220301.cho', '20220301.chr', '20220301.chy', '20220301.ckb', '20220301.co', '20220301.cr', '20220301.crh', '20220301.cs', '20220301.csb', '20220301.cu', '20220301.cv', '20220301.cy', '20220301.da', '20220301.de', '20220301.din', '20220301.diq', '20220301.dsb', '20220301.dty', '20220301.dv', '20220301.dz', '20220301.ee', '20220301.el', '20220301.eml', '20220301.en', '20220301.eo', '20220301.es', '20220301.et', '20220301.eu', '20220301.ext', '20220301.fa', '20220301.ff', '20220301.fi', '20220301.fiu-vro', '20220301.fj', '20220301.fo', '20220301.fr', '20220301.frp', '20220301.frr', '20220301.fur', '20220301.fy', '20220301.ga', '20220301.gag', '20220301.gan', '20220301.gd', '20220301.gl', '20220301.glk', '20220301.gn', '20220301.gom', '20220301.gor', '20220301.got', '20220301.gu', '20220301.gv', '20220301.ha', '20220301.hak', '20220301.haw', '20220301.he', '20220301.hi', '20220301.hif', '20220301.ho', '20220301.hr', '20220301.hsb', '20220301.ht', '20220301.hu', '20220301.hy', '20220301.ia', '20220301.id', '20220301.ie', '20220301.ig', '20220301.ii', '20220301.ik', '20220301.ilo', '20220301.inh', '20220301.io', '20220301.is', '20220301.it', '20220301.iu', '20220301.ja', '20220301.jam', '20220301.jbo', '20220301.jv', '20220301.ka', '20220301.kaa', '20220301.kab', '20220301.kbd', '20220301.kbp', '20220301.kg', '20220301.ki', '20220301.kj', '20220301.kk', '20220301.kl', '20220301.km', '20220301.kn', '20220301.ko', '20220301.koi', '20220301.krc', '20220301.ks', '20220301.ksh', '20220301.ku', '20220301.kv', '20220301.kw', '20220301.ky', '20220301.la', '20220301.lad', '20220301.lb', '20220301.lbe', '20220301.lez', '20220301.lfn', '20220301.lg', '20220301.li', '20220301.lij', '20220301.lmo', '20220301.ln', '20220301.lo', '20220301.lrc', '20220301.lt', '20220301.ltg', '20220301.lv', '20220301.mai', '20220301.map-bms', '20220301.mdf', '20220301.mg', '20220301.mh', '20220301.mhr', '20220301.mi', '20220301.min', '20220301.mk', '20220301.ml', '20220301.mn', '20220301.mr', '20220301.mrj', '20220301.ms', '20220301.mt', '20220301.mus', '20220301.mwl', '20220301.my', '20220301.myv', '20220301.mzn', '20220301.na', '20220301.nah', '20220301.nap', '20220301.nds', '20220301.nds-nl', '20220301.ne', '20220301.new', '20220301.ng', '20220301.nl', '20220301.nn', '20220301.no', '20220301.nov', '20220301.nrm', '20220301.nso', '20220301.nv', '20220301.ny', '20220301.oc', '20220301.olo', '20220301.om', '20220301.or', '20220301.os', '20220301.pa', '20220301.pag', '20220301.pam', '20220301.pap', '20220301.pcd', '20220301.pdc', '20220301.pfl', '20220301.pi', '20220301.pih', '20220301.pl', '20220301.pms', '20220301.pnb', '20220301.pnt', '20220301.ps', '20220301.pt', '20220301.qu', '20220301.rm', '20220301.rmy', '20220301.rn', '20220301.ro', '20220301.roa-rup', '20220301.roa-tara', '20220301.ru', '20220301.rue', '20220301.rw', '20220301.sa', '20220301.sah', '20220301.sat', '20220301.sc', '20220301.scn', '20220301.sco', '20220301.sd', '20220301.se', '20220301.sg', '20220301.sh', '20220301.si', '20220301.simple', '20220301.sk', '20220301.sl', '20220301.sm', '20220301.sn', '20220301.so', '20220301.sq', '20220301.sr', '20220301.srn', '20220301.ss', '20220301.st', '20220301.stq', '20220301.su', '20220301.sv', '20220301.sw', '20220301.szl', '20220301.ta', '20220301.tcy', '20220301.te', '20220301.tet', '20220301.tg', '20220301.th', '20220301.ti', '20220301.tk', '20220301.tl', '20220301.tn', '20220301.to', '20220301.tpi', '20220301.tr', '20220301.ts', '20220301.tt', '20220301.tum', '20220301.tw', '20220301.ty', '20220301.tyv', '20220301.udm', '20220301.ug', '20220301.uk', '20220301.ur', '20220301.uz', '20220301.ve', '20220301.vec', '20220301.vep', '20220301.vi', '20220301.vls', '20220301.vo', '20220301.wa', '20220301.war', '20220301.wo', '20220301.wuu', '20220301.xal', '20220301.xh', '20220301.xmf', '20220301.yi', '20220301.yo', '20220301.za', '20220301.zea', '20220301.zh', '20220301.zh-classical', '20220301.zh-min-nan', '20220301.zh-yue', '20220301.zu']
Please suggest what should be done.
Thank you!
Do you download the key status for layers [4, 5, 6, 7, 8] in llama2?
examples
├── data
│ ├── stats
│ │ └── ._hugging_cache_llama-2-7b
│ │ └── wikipedia_stats
│ │ ├── model.layers.4.mlp.down_proj_float32_mom2_100000.npz
│ │ ├── model.layers.5.mlp.down_proj_float32_mom2_100000.npz
│ │ ├── model.layers.6.mlp.down_proj_float32_mom2_100000.npz
│ │ ├── model.layers.7.mlp.down_proj_float32_mom2_100000.npz
│ │ └── model.layers.8.mlp.down_proj_float32_mom2_100000.npz
└── └── zsre_mend_eval_portability_gpt4.json
Yes done that!
Make sure that the directory for your load model in yaml is ./hugging_cache/llama-2-7b
, you can refer to the code here.
filename
exists. If it can route to filename
, you do not need to download the wikipedia dataset, and your error will not occur.If your problem has been resolved, please help close this issue.
Hi,
Yes, it has been resolved now. Thank you so much for the guidance and the quick response!
Hi, while I am trying to run the google colab notebook,
1) I am getting the error:
"OSError: Can't load the configuration of '/mnt/peng/EasyEdit/hugging_cache/llama-2-7b'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure '/mnt/peng/EasyEdit/hugging_cache/llama-2-7b' is the correct path to a directory containing a config.json file"
Please advice what should be done.
2) Approximately how much time would this editing command take for execution? python run_zsre_llama2.py \ --editing_method=ROME \ --hparams_dir=../hparams/ROME/llama-7b \ --data_dir=./data
In my case it is stuck after displaying this line: Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████| 2/2 [00:13<00:00, 6.51s/it]